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Field
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selection criteria Experience with AI / probabilistic AI / Machine Learning / Reinforcement Learning Experience with numerical optimization and MPC Strong programming skills (Python, C) Personal
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Experience with AI / probabilistic AI / Machine Learning Experience with numerical optimization and MPC Strong programming skills (Python, C) Experience with predictive maintenance, fatigue, fault detection
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) for general criteria for the position. Preferred selection criteria Experience with AI / probabilistic AI / Machine Learning Experience with numerical optimization and MPC Strong programming skills (Python, C
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peak competences in decision-making, optimization, control, and AI for cyber-physical systems. The position will be part of the research centre the Norwegian Center on AI for Decision (aiD) cooperating
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of the position. The successful candidate will have a solid theoretical foundation in one or more of the topics: Computational Mechanics, Finite Element Analysis (FEA), Numerical Optimization
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30 kHz by a burst laser source combined to two Optical Parametric Oscillators (OPOs) [7] applied on H2/air flame. The optimization of OH and NO excitation strategies and the quantification
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innovative projects that have a major impact on society. Context and contributions of the position: Understanding subsurface fluid flow is crucial for optimizing geothermal systems and mitigating risks such as
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– derivatives, wave functions, linear algebra, differential equations, numerical optimization. Some background in solid-state physics, optics, electrical engineering, chemistry, and/or materials science
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numerical solution properties, allowing efficient solution, with a computation time between a few seconds and a few minutes for a mediumsized problem with a few hundred optimization variables, and easy
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wide range of opportunities. In connection with the cooperation in one of the numerous research and working groups, we also closely cooperate with the German Institute of Human Nutrition, who